OpenRaiser/Intern-Atlas huggingface dataset:
Detecting Changes Between Optical Images of Different Spatial and Spectral Resolutions: a Fusion-Based Approach
Venue: arXiv cs.CV 2016
Year: 2016

Change detection is one of the most challenging issues when analyzing remotely sensed images. Comparing several multi-date images acquired through the same kind of sensor is the most common scenario. Conversely, designing robust, flexible and scalable algorithms for change detection becomes even more challenging when the images have been acquired by two different kinds of sensors. This situation arises in case of emergency under critical constraints. This paper presents, to the best of authors' knowledge, the first strategy to deal with optical images characterized by dissimilar spatial and spectral resolutions. Typical considered scenarios include change detection between panchromatic or multispectral and hyperspectral images. The proposed strategy consists of a 3-step procedure: i) inferring a high spatial and spectral resolution image by fusion of the two observed images characterized one by a low spatial resolution and the other by a low spectral resolution, ii) predicting two images with respectively the same spatial and spectral resolutions as the observed images by degradation of the fused one and iii) implementing a decision rule to each pair of observed and predicted images characterized by the same spatial and spectral resolutions to identify changes. The performance of the proposed framework is evaluated on real images with simulated realistic changes.
mlr Tutorial
Venue: arXiv cs.LG 2016
Year: 2016

This document provides and in-depth introduction to the mlr framework for machine learning experiments in R.
Power-Domain Non-Orthogonal Multiple Access (NOMA) in 5G Systems: Potentials and Challenges
Venue: arXiv cs.IT 2016
Year: 2016

Non-orthogonal multiple access (NOMA) is one of the promising radio access techniques for performance enhancement in next-generation cellular communications. Compared to orthogonal frequency division multiple access (OFDMA), which is a well-known high-capacity orthogonal multiple access (OMA) technique, NOMA offers a set of desirable benefits, including greater spectrum efficiency. There are different types of NOMA techniques, including power-domain and code-domain. This paper primarily focuses on power-domain NOMA that utilizes superposition coding (SC) at the transmitter and successive interference cancellation (SIC) at the receiver. Various researchers have demonstrated that NOMA can be used effectively to meet both network-level and user-experienced data rate requirements of fifth-generation (5G) technologies. From that perspective, this paper comprehensively surveys the recent progress of NOMA in 5G systems, reviewing the state-of-the-art capacity analysis, power allocation strategies, user fairness, and user-pairing schemes in NOMA. In addition, this paper discusses how NOMA performs when it is integrated with various proven wireless communications techniques, such as cooperative communications, multiple input multiple output (MIMO), beamforming, space time coding, and network coding, among others. Furthermore, this paper discusses several important issues on NOMA implementation and provides some avenues for future research.
Design of Admissible Heuristics for Kinodynamic Motion Planning via Sum-of-Squares Programming
Venue: arXiv cs.RO 2016
Year: 2016

How does one obtain an admissible heuristic for a kinodynamic motion planning problem? This paper develops the analytical tools and techniques to answer this question. A sufficient condition for the admissibility of a heuristic is presented which can be checked directly from the problem data. This condition is also used to formulate a concave program to optimize an admissible heuristic. This optimization is then approximated and solved in polynomial time using sum-of-squares programming techniques. A number of examples are provided to demonstrate these concepts.
Low-power Spin Valve Logic using Spin-transfer Torque with Automotion of Domain Walls
Venue: arXiv cs.ET 2016
Year: 2016

A novel scheme for non-volatile digital computation is proposed using spin-transfer torque (STT) and automotion of magnetic domain walls (DWs). The basic computing element is composed of a lateral spin valve (SV) with two ferromagnetic (FM) wires served as interconnects, where DW automotion is used to propagate the information from one device to another. The non-reciprocity of both device and interconnect is realized by sizing different contact areas at the input and the output as well as enhancing the local damping mechanism. The proposed logic is suitable for scaling due to a high energy barrier provided by a long FM wire. Compared to the scheme based on non-local spin valves (NLSVs) in the previous proposal, the devices can be operated at lower current density due to utilizing all injected spins for local magnetization reversals, and thus improve both energy efficiency and resistance to electromigration. This device concept is justified by simulating a buffer, an inverter, and a 3-input majority gate with comprehensive numerical simulations, including spin transport through the FM/non-magnetic (NM) interfaces as well as the NM channel and stochastic magnetization dynamics inside FM wires. In addition to digital computing, the proposed framework can also be used as a transducer between DWs and spin currents for higher wiring flexibility in the interconnect network.
KU-ISPL Language Recognition System for NIST 2015 i-Vector Machine Learning Challenge
Venue: arXiv cs.SD 2016
Year: 2016

In language recognition, the task of rejecting/differentiating closely spaced versus acoustically far spaced languages remains a major challenge. For confusable closely spaced languages, the system needs longer input test duration material to obtain sufficient information to distinguish between languages. Alternatively, if languages are distinct and not acoustically/linguistically similar to others, duration is not a sufficient remedy. The solution proposed here is to explore duration distribution analysis for near/far languages based on the Language Recognition i-Vector Machine Learning Challenge 2015 (LRiMLC15) database. Using this knowledge, we propose a likelihood ratio based fusion approach that leveraged both score and duration information. The experimental results show that the use of duration and score fusion improves language recognition performance by 5% relative in LRiMLC15 cost.
Model Checking Spatial Logics for Closure Spaces
Venue: arXiv cs.LO 2016
Year: 2016

Spatial aspects of computation are becoming increasingly relevant in Computer Science, especially in the field of collective adaptive systems and when dealing with systems distributed in physical space. Traditional formal verification techniques are well suited to analyse the temporal evolution of programs; however, properties of space are typically not taken into account explicitly. We present a topology-based approach to formal verification of spatial properties depending upon physical space. We define an appropriate logic, stemming from the tradition of topological interpretations of modal logics, dating back to earlier logicians such as Tarski, where modalities describe neighbourhood. We lift the topological definitions to the more general setting of closure spaces, also encompassing discrete, graph-based structures. We extend the framework with a spatial surrounded operator, a propagation operator and with some collective operators. The latter are interpreted over arbitrary sets of points instead of individual points in space. We define efficient model checking procedures, both for the individual and the collective spatial fragments of the logic and provide a proof-of-concept tool.
Decoupled Uplink and Downlink in a Wireless System with Buffer-Aided Relaying
Venue: arXiv cs.IT 2016
Year: 2016

The paper treats a multiuser relay scenario where multiple user equipments (UEs) have a two-way communication with a common Base Station (BS) in the presence of a buffer-equipped Relay Station (RS). Each of the uplink (UL) and downlink (DL) transmission can take place over a direct or over a relayed path. Traditionally, the UL and the DL path of a given two-way link are coupled, that is, either both are direct links or both are relayed links. By removing the restriction for coupling, one opens the design space for a decoupled two-way links. Following this, we devise two protocols: orthogonal decoupled UL/DL buffer-aided (ODBA) relaying protocol and non-orthogonal decoupled UL/DL buffer-aided (NODBA) relaying protocol. In NODBA, the receiver can use successive interference cancellation (SIC) to extract the desired signal from a collision between UL and DL signals. For both protocols, we characterize the transmission decision policies in terms of maximization of the average two-way sum rate of the system. The numerical results show that decoupling association and non-orthogonal radio access lead to significant throughput gains for two-way traffic.
Weakly supervised spoken term discovery using cross-lingual side information
Venue: arXiv cs.CL 2016
Year: 2016

Recent work on unsupervised term discovery (UTD) aims to identify and cluster repeated word-like units from audio alone. These systems are promising for some very low-resource languages where transcribed audio is unavailable, or where no written form of the language exists. However, in some cases it may still be feasible (e.g., through crowdsourcing) to obtain (possibly noisy) text translations of the audio. If so, this information could be used as a source of side information to improve UTD. Here, we present a simple method for rescoring the output of a UTD system using text translations, and test it on a corpus of Spanish audio with English translations. We show that it greatly improves the average precision of the results over a wide range of system configurations and data preprocessing methods.
Systematic Mapping Protocol: Have Systematic Reuse Benefits Been Transferred to Real-world Settings?
Venue: arXiv cs.SE 2016
Year: 2016

This document details the planning phase of a Systematic Mapping Study. Our goal is to identify and to understand the benefits that the software engineering community has reported on the application of the different reuse strategies in industrial context, by building a general picture (map) containing: the claimed benefits, the data supporting those claims, the industry's domains and the reuse strategy employed.
Semi-supervised knowledge extraction for detection of drugs and their effects
Venue: arXiv cs.CL 2016
Year: 2016

New Psychoactive Substances (NPS) are drugs that lay in a grey area of legislation, since they are not internationally and officially banned, possibly leading to their not prosecutable trade. The exacerbation of the phenomenon is that NPS can be easily sold and bought online. Here, we consider large corpora of textual posts, published on online forums specialized on drug discussions, plus a small set of known substances and associated effects, which we call seeds. We propose a semi-supervised approach to knowledge extraction, applied to the detection of drugs (comprising NPS) and effects from the corpora under investigation. Based on the very small set of initial seeds, the work highlights how a contrastive approach and context deduction are effective in detecting substances and effects from the corpora. Our promising results, which feature a F1 score close to 0.9, pave the way for shortening the detection time of new psychoactive substances, once these are discussed and advertised on the Internet.
Twitter Opinion Topic Model: Extracting Product Opinions from Tweets by Leveraging Hashtags and Sentiment Lexicon
Venue: arXiv cs.CL 2016
Year: 2016

Aspect-based opinion mining is widely applied to review data to aggregate or summarize opinions of a product, and the current state-of-the-art is achieved with Latent Dirichlet Allocation (LDA)-based model. Although social media data like tweets are laden with opinions, their "dirty" nature (as natural language) has discouraged researchers from applying LDA-based opinion model for product review mining. Tweets are often informal, unstructured and lacking labeled data such as categories and ratings, making it challenging for product opinion mining. In this paper, we propose an LDA-based opinion model named Twitter Opinion Topic Model (TOTM) for opinion mining and sentiment analysis. TOTM leverages hashtags, mentions, emoticons and strong sentiment words that are present in tweets in its discovery process. It improves opinion prediction by modeling the target-opinion interaction directly, thus discovering target specific opinion words, neglected in existing approaches. Moreover, we propose a new formulation of incorporating sentiment prior information into a topic model, by utilizing an existing public sentiment lexicon. This is novel in that it learns and updates with the data. We conduct experiments on 9 million tweets on electronic products, and demonstrate the improved performance of TOTM in both quantitative evaluations and qualitative analysis. We show that aspect-based opinion analysis on massive volume of tweets provides useful opinions on products.
Image Denoising via Multi-scale Nonlinear Diffusion Models
Venue: arXiv cs.CV 2016
Year: 2016

Image denoising is a fundamental operation in image processing and holds considerable practical importance for various real-world applications. Arguably several thousands of papers are dedicated to image denoising. In the past decade, sate-of-the-art denoising algorithm have been clearly dominated by non-local patch-based methods, which explicitly exploit patch self-similarity within image. However, in recent two years, discriminatively trained local approaches have started to outperform previous non-local models and have been attracting increasing attentions due to the additional advantage of computational efficiency. Successful approaches include cascade of shrinkage fields (CSF) and trainable nonlinear reaction diffusion (TNRD). These two methods are built on filter response of linear filters of small size using feed forward architectures. Due to the locality inherent in local approaches, the CSF and TNRD model become less effective when noise level is high and consequently introduces some noise artifacts. In order to overcome this problem, in this paper we introduce a multi-scale strategy. To be specific, we build on our newly-developed TNRD model, adopting the multi-scale pyramid image representation to devise a multi-scale nonlinear diffusion process. As expected, all the parameters in the proposed multi-scale diffusion model, including the filters and the influence functions across scales, are learned from training data through a loss based approach. Numerical results on Gaussian and Poisson denoising substantiate that the exploited multi-scale strategy can successfully boost the performance of the original TNRD model with single scale. As a consequence, the resulting multi-scale diffusion models can significantly suppress the typical incorrect features for those noisy images with heavy noise.
Characterization of Lung Nodule Malignancy using Hybrid Shape and Appearance Features
Venue: arXiv cs.CV 2016
Year: 2016

Computed tomography imaging is a standard modality for detecting and assessing lung cancer. In order to evaluate the malignancy of lung nodules, clinical practice often involves expert qualitative ratings on several criteria describing a nodule's appearance and shape. Translating these features for computer-aided diagnostics is challenging due to their subjective nature and the difficulties in gaining a complete description. In this paper, we propose a computerized approach to quantitatively evaluate both appearance distinctions and 3D surface variations. Nodule shape was modeled and parameterized using spherical harmonics, and appearance features were extracted using deep convolutional neural networks. Both sets of features were combined to estimate the nodule malignancy using a random forest classifier. The proposed algorithm was tested on the publicly available Lung Image Database Consortium dataset, achieving high accuracy. By providing lung nodule characterization, this method can provide a robust alternative reference opinion for lung cancer diagnosis.
Seeing is Believing: A Unified Model for Consistency and Isolation via States
Venue: arXiv cs.DC 2016
Year: 2016

This paper introduces a unified model of consistency and isolation that minimizes the gap between how these guarantees are defined and how they are perceived. Our approach is premised on a simple observation: applications view storage systems as black-boxes that transition through a series of states, a subset of which are observed by applications. For maximum clarity, isolation and consistency guarantees should be expressed as constraints on those states. Instead, these properties are currently expressed as constraints on operation histories that are not visible to the application. We show that adopting a state-based approach to expressing these guarantees brings forth several benefits. First, it makes it easier to focus on the anomalies that a given isolation or consistency level allows (and that applications must deal with), rather than those that it proscribes. Second, it unifies the often disparate theories of isolation and consistency and provides a structure for composing these guarantees. We leverage this modularity to apply to transactions (independently of the isolation level under which they execute) the equivalence between causal consistency and session guarantees that Chockler et al. had proved for single operations. Third, it brings clarity to the increasingly crowded field of proposed consistency and isolation properties by winnowing spurious distinctions: we find that the recently proposed parallel snapshot isolation introduced by Sovran et al. is in fact a specific implementation of an older guarantee, lazy consistency (or PL-2+), introduced by Adya et al.
21st Century Computer Architecture
Venue: arXiv cs.CY 2016
Year: 2016

Because most technology and computer architecture innovations were (intentionally) invisible to higher layers, application and other software developers could reap the benefits of this progress without engaging in it. Higher performance has both made more computationally demanding applications feasible (e.g., virtual assistants, computer vision) and made less demanding applications easier to develop by enabling higher-level programming abstractions (e.g., scripting languages and reusable components). Improvements in computer system cost-effectiveness enabled value creation that could never have been imagined by the field's founders (e.g., distributed web search sufficiently inexpensive so as to be covered by advertising links). The wide benefits of computer performance growth are clear. Recently, Danowitz et al. apportioned computer performance growth roughly equally between technology and architecture, with architecture credited with ~80x improvement since 1985. As semiconductor technology approaches its "end-of-the-road" (see below), computer architecture will need to play an increasing role in enabling future ICT innovation. But instead of asking, "How can I make my chip run faster?," architects must now ask, "How can I enable the 21st century infrastructure, from sensors to clouds, adding value from performance to privacy, but without the benefit of near-perfect technology scaling?". The challenges are many, but with appropriate investment, opportunities abound. Underlying these opportunities is a common theme that future architecture innovations will require the engagement of and investments from innovators in other ICT layers.
Decoupled Asynchronous Proximal Stochastic Gradient Descent with Variance Reduction
Venue: arXiv cs.LG 2016
Year: 2016

In the era of big data, optimizing large scale machine learning problems becomes a challenging task and draws significant attention. Asynchronous optimization algorithms come out as a promising solution. Recently, decoupled asynchronous proximal stochastic gradient descent (DAP-SGD) is proposed to minimize a composite function. It is claimed to be able to off-loads the computation bottleneck from server to workers by allowing workers to evaluate the proximal operators, therefore, server just need to do element-wise operations. However, it still suffers from slow convergence rate because of the variance of stochastic gradient is nonzero. In this paper, we propose a faster method, decoupled asynchronous proximal stochastic variance reduced gradient descent method (DAP-SVRG). We prove that our method has linear convergence for strongly convex problem. Large-scale experiments are also conducted in this paper, and results demonstrate our theoretical analysis.
Strategic Seeding of Rival Opinions
Venue: arXiv cs.GT 2016
Year: 2016

We present a network influence game that models players strategically seeding the opinions of nodes embedded in a social network. A social learning dynamic, whereby nodes repeatedly update their opinions to resemble those of their neighbors, spreads the seeded opinions through the network. After a fixed period of time, the dynamic halts and each player's utility is determined by the relative strength of the opinions held by each node in the network vis-a-vis the other players. We show that the existence of a pure Nash equilibrium cannot be guaranteed in general. However, if the dynamics are allowed to progress for a sufficient amount of time so that a consensus among all of the nodes is obtained, then the existence of a pure Nash equilibrium can be guaranteed. The computational complexity of finding a pure strategy best response is shown to be NP-complete, but can be efficiently approximated to within a (1 - 1/e) factor of optimal by a simple greedy algorithm.
Posted Pricing sans Discrimination
Venue: arXiv cs.GT 2016
Year: 2016

In the quest for market mechanisms that are easy to implement, yet close to optimal, few seem as viable as posted pricing. Despite the growing body of impressive results, the performance of most posted price mechanisms however, rely crucially on price discrimination when multiple copies of a good are available. For the more general case with non-linear production costs on each good, hardly anything is known for general multi-good markets. With this in mind, we study a Bayesian setting where the seller can produce any number of copies of a good but faces convex production costs for the same, and buyers arrive sequentially. Our main contribution is a framework for non-discriminatory pricing in the presence of production costs: the framework yields posted price mechanisms with O(1)-approximation factors for fractionally subadditive (XoS) buyers, logarithmic approximations for subadditive buyers, and also extends to settings where the seller is oblivious to buyer valuations. Our work presents the first known results for Bayesian settings with production costs and is among the few posted price mechanisms that do not charge buyers differently for the same good.